Multiclass segmentation of suspicious findings in simulated breast tomosynthesis images using a U-Net

Yann N. G. da Nobrega; Giulia Carvalhal; Joao P. V. Teixeira; Barbara P. de Camargo; Thais G. do Rego; Yuri Malheiros; Telmo M. E. Silva Filho; Trevor L. Vent; Raymond J. Acciavatti; Andrew D. A. Maidment; Bruno Barufaldi

doi:10.1117/12.2626225

13 July 2022 Multiclass segmentation of suspicious findings in simulated breast tomosynthesis images using a U-Net

Yann N. G. da Nobrega, Giulia Carvalhal, Joao P. V. Teixeira, Barbara P. de Camargo, Thais G. do Rego, Yuri Malheiros, Telmo M. E. Silva Filho, Trevor L. Vent, Raymond J. Acciavatti, Andrew D. A. Maidment, Bruno Barufaldi

Author Affiliations +

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860L (2022) https://doi.org/10.1117/12.2626225
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

Our lab has built a next-generation tomosynthesis (NGT) system utilizing scanning motions with more degrees of freedom than clinical digital breast tomosynthesis systems. We are working toward designing scanning motions that are customized around the locations of suspicious findings. The first step in this direction is to demonstrate that these findings can be detected with a single projection image, which can guide the remainder of the scan. This paper develops an automated method to identify findings that are prone to be masked. Perlin-noise phantoms and synthetic lesions were used to simulate masked cancers. NGT projections of phantoms were simulated using ray-tracing software. The risk of masking cancers was mapped using the ground-truth labels of phantoms. The phantom labels were used to denote regions of low and high risk of masking suspicious findings. A U-Net model was trained for multiclass segmentation of phantom images. Model performance was quantified with a receiver operating characteristic (ROC) curve using area under the curve (AUC). The ROC operating point was defined to be the point closest to the upper left corner of ROC space. The output predictions showed an accurate segmentation of tissue predominantly adipose (mean AUC of 0.93). The predictions also indicate regions of suspicious findings; for the highest risk class, mean AUC was 0.89, with a true positive rate of 0.80 and a true negative rate of 0.83 at the operating point. In summary, this paper demonstrates with virtual phantoms that a single projection can indeed be used to identify suspicious findings.

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Yann N. G. da Nobrega, Giulia Carvalhal, Joao P. V. Teixeira, Barbara P. de Camargo, Thais G. do Rego, Yuri Malheiros, Telmo M. E. Silva Filho, Trevor L. Vent, Raymond J. Acciavatti, Andrew D. A. Maidment, and Bruno Barufaldi "Multiclass segmentation of suspicious findings in simulated breast tomosynthesis images using a U-Net", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860L (13 July 2022); https://doi.org/10.1117/12.2626225

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KEYWORDS

Image segmentation

Breast

Tissues

Cancer

Digital breast tomosynthesis

Computer simulations

Motion models

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